SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results

Leiner, Johannes; Pellissier, Vincent; Nitsche, Anne; König, Sebastian; Hohenstein, Sven; Nachtigall, Irit; Hindricks, Gerhard; Kutschker, Christoph; Rolinski, Boris; Gebauer, Julian; Prantz, Anja; Schubert, Jöerg; Patzschke, Joerg; Bollmann, Andreas; Wolz, Martin

doi:10.1016/j.ijid.2021.09.008

Cited by 4 publications

(5 citation statements)

References 35 publications

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“… 6 A model that includes patient demographic data, laboratory, and symptoms achieved high performance with AUROC of 0.97 to predict COVID-19-positive cases in a German study. 7 In the COVID-19 endemic state, screening for COVID-19 using RT-PCR and ART upon hospital admission may not be cost-effective. Our model without ART in ED performed less favorably with AUROC of 0.89 (sensitivity and specificity of 70.6% and 91.7%, respectively) versus 0.95 (sensitivity and specificity of 91.3% and 99.1%, respectively) with addition of ART.…”

Section: Discussionmentioning

confidence: 99%

“…These variables were selected based on data availability and previous reports on COVID-19 risk predictors. [6][7][8][9] Patients who had received government COVID-19 restriction orders were electronically tagged in the hospital electronic medical records (EMRs) from which all required data were extracted.…”

Section: Variables Included In the Analysesmentioning

confidence: 99%

“…Several COVID-19 prediction models that include standard laboratory tests, patient demographic data, exposure history, and symptoms have been developed and tested. 6,7 A study conducted by Ducray et al on the diagnostic performance of chest computed tomography showed sensitivity of 90.2% in detecting COVID-19. 8 As previous studies on COVID-19 predictive models did not differentiate infectious COVID-19, we aimed to develop and assess prediction models for infectious COVID-19 (Ct <25) in our ED setting.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction model to identify infectious COVID-19 patients in the emergency department

Aung,

Venkatachalam,

Sim

et al. 2024

ASHE

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Background: Real-time reverse-transcriptase polymerase chain reaction (RT-PCR) has been the gold standard for diagnosing coronavirus disease 2019 (COVID-19) but has a lag time for the results. An effective prediction algorithm for infectious COVID-19, utilized at the emergency department (ED), may reduce the risk of healthcare-associated COVID-19. Objective: To develop a prototypic prediction model for infectious COVID-19 at the time of presentation to the ED. Material and methods: Retrospective cohort study of all adult patients admitted to Singapore General Hospital (SGH) through ED between March 15, 2020, and December 31, 2022, with admission of COVID-19 RT-PCR results. Two prediction models were developed and evaluated using area under the curve (AUC) of receiver operating characteristics (ROC) to identify infectious COVID-19 patients (cycle threshold (Ct) of <25). Results: Total of 78,687 patients were admitted to SGH through ED during study period. 6,132 of them tested severe acute respiratory coronavirus 2 positive on RT-PCR. Nearly 70% (4,226 of 6,132) of the patients had infectious COVID-19 (Ct<25). Model that included demographics, clinical history, symptom and laboratory variables had AUROC of 0.85 with sensitivity and specificity of 80.0% & 72.1% respectively. When antigen rapid test results at ED were available and added to the model for a subset of the study population, AUROC reached 0.97 with sensitivity and specificity of 95.0% and 92.8% respectively. Both models maintained respective sensitivity and specificity results when applied to validation data. Conclusion: Clinical predictive models based on available information at ED can be utilized for identification of infectious COVID-19 patients and may enhance infection prevention efforts.

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Section: Discussionmentioning

confidence: 99%

Section: Variables Included In the Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction model to identify infectious COVID-19 patients in the emergency department

Aung,

Venkatachalam,

Sim

et al. 2024

ASHE

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“…RT-PCR can be detected even after replication because it amplifies and tests RNA [ 27 ]. However, if this is not the case, false-negative RATs may potentially transmit SARS-CoV-2 in high prevalence, as appropriate control and protection measures may not be implemented [ 30 ]. Therefore, it is necessary to strategically determine the best testing method, such as RATs serial testing and RT-PCR parallel testing for screening when using RATs to improve the usefulness in high prevalence [ 31 32 ].…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Rapid Antigen Screening Tests for SARS-CoV-2 Infection at Correctional Facilities in Korea: March - May 2022

Lee,

Park,

et al. 2023

Infect Chemother

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Background The number of confirmed cases of individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection increased rapidly due to the Omicron variant. Correctional facilities are vulnerable to infectious diseases, and they introduced rapid antigen tests (RATs) to allow for early detection and rapid response. We aimed to evaluate the diagnostic performance and usefulness of SARS-CoV-2 RATs in newly incarcerated people. Materials and Methods We conducted a cross-sectional study at correctional facilities in Korea from 9 March to 22 May 2022. The study population was newly incarcerated people who were divided into two groups. In one group, 799 paired SARS-CoV-2 RATs and real-time polymerase chain reaction (RT-PCR) were conducted simultaneously in 522 individuals in March 2022. In the other group, 4,034 paired RATs and RT-PCR consecutively in 4,034 participants; only individuals with negative RATs results underwent RT-PCR from April to May 2022. We analyzed data using descriptive statistics and a logistic regression model. Results Among the 799 specimens in March, RT-PCR was positive in 72 (9.0%), and among the 4,034 specimens in April - May 2022, RT-PCR was positive in 40 (1.0%). Overall, the RATs had a sensitivity of 58.3%, specificity of 100.0%, positive predictive value (PPV) of 100.0%, and negative predictive value (NPV) of 96.0%. Asymptomatic individuals constituted 98.2% of the study group, and symptomatic individuals 1.8%. In asymptomatic cases, the sensitivity of RATs was 52.5%, specificity was 100.0%, PPV was 100.0%, and NPV was 96.3%. In symptomatic cases, the sensitivity of RATs was 84.6%, specificity was 100.0%, PPV was 100.0%, and NPV was 33.3%. Sensitivity ( P = 0.034) and NPV ( P = 0.004) differed significantly according to the presence and absence of symptoms, and the F1 score was the highest at 0.9 in symptomatic individuals in March. There was a positive linear trend in the proportion of false-negative RATs in newly incarcerated people following the weekly incidence of SARS-CoV-2 ( P = 0.033). The best-associated predictors of RATs for SARS-CoV-2 infection involved symptoms, timing of sample collection, and repeat testing. Conclusion Sensitivity and NPV significantly depend on whether symptoms are present, and the percentage of false negatives is correlated with the incidence. Thus, using RATs should be adjusted according to the presence or absence of symptoms and the incidence of SARS-CoV-2 infection in the community. RATs could be a useful screening tool as an effective first-line countermeasure because they can rapidly identify infectious patients and minimize SARS-CoV-2 transmission in correctional facilities.

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Clinical accuracy of instrument-based SARS-CoV-2 antigen diagnostic tests: a systematic review and meta-analysis

Manten,

Katzenschlager,

Brümmer

et al. 2024

Virol J

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Background During the COVID-19 pandemic, antigen diagnostic tests were frequently used for screening, triage, and diagnosis. Novel instrument-based antigen tests (iAg tests) hold the promise of outperforming their instrument-free, visually-read counterparts. Here, we provide a systematic review and meta-analysis of the SARS-CoV-2 iAg tests’ clinical accuracy. Methods We systematically searched MEDLINE (via PubMed), Web of Science, medRxiv, and bioRxiv for articles published before November 7th, 2022, evaluating the accuracy of iAg tests for SARS-CoV-2 detection. We performed a random effects meta-analysis to estimate sensitivity and specificity and used the QUADAS-2 tool to assess study quality and risk of bias. Sub-group analysis was conducted based on Ct value range, IFU-conformity, age, symptom presence and duration, and the variant of concern. Results We screened the titles and abstracts of 20,431 articles and included 114 publications that fulfilled the inclusion criteria. Additionally, we incorporated three articles sourced from the FIND website, totaling 117 studies encompassing 95,181 individuals, which evaluated the clinical accuracy of 24 commercial COVID-19 iAg tests. The studies varied in risk of bias but showed high applicability. Of 24 iAg tests from 99 studies assessed in the meta-analysis, the pooled sensitivity and specificity compared to molecular testing of a paired NP swab sample were 76.7% (95% CI 73.5 to 79.7) and 98.4% (95% CI 98.0 to 98.7), respectively. Higher sensitivity was noted in individuals with high viral load (99.6% [95% CI 96.8 to 100] at Ct-level ≤ 20) and within the first week of symptom onset (84.6% [95% CI 78.2 to 89.3]), but did not differ between tests conducted as per manufacturer’s instructions and those conducted differently, or between point-of-care and lab-based testing. Conclusion Overall, iAg tests have a high pooled specificity but a moderate pooled sensitivity, according to our analysis. The pooled sensitivity increases with lower Ct-values (a proxy for viral load), or within the first week of symptom onset, enabling reliable identification of most COVID-19 cases and highlighting the importance of context in test selection. The study underscores the need for careful evaluation considering performance variations and operational features of iAg tests.

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SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results

Cited by 4 publications

References 35 publications

Prediction model to identify infectious COVID-19 patients in the emergency department

Prediction model to identify infectious COVID-19 patients in the emergency department

Accuracy of Rapid Antigen Screening Tests for SARS-CoV-2 Infection at Correctional Facilities in Korea: March - May 2022

Clinical accuracy of instrument-based SARS-CoV-2 antigen diagnostic tests: a systematic review and meta-analysis

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